A popular resource on the World Wide Web is the question-and-answer (Q&A) community, where users can post questions on a community website for other community members to answer. There are several designs for this type of website, including open forums where anyone can answer any question and expert forums where experts, self described or otherwise, can answer a posed question.
Assuming a community of experts associated or tagged with areas of known expertise (self-described or inferred), there are several known methods for routing each question asked to the expert or experts best qualified, according to the known routing method, to answer it.
The simplest known routing method is text matching. Text matching typically involves matching text or keywords included in a question to an expertise. A drawback to this approach is that questions are apt to be specific while expertise descriptions are apt to be general. For example, it is difficult to find an expert using text matching to answer the question “How do I hit a sand wedge?” because it is unlikely that an expert would describe his expertise as “sand wedge” or “hitting the sand wedge.” Instead, it would be common to find an expert claiming an expertise in “golf”.
Thus, use of text matching to route experts to a question has the obvious disadvantage that if a topic or area of expertise truly associated with the question does not literally match the text of the question, the question will be improperly routed to experts in the areas of expertise that literally match the text of the question, not the topic or area of expertise truly associated with the question. “How do I hit a sand wedge?” might be routed to an expert in “sand”.
Another known routing method is manual categorization. This routing method typically requires a user to manually categorize a question. For example, a user may categorize a question with keywords that match expertise tags, assign a category or categories to a question, and/or manually select an expert to answer his/her question from a list of experts.
Disadvantages to routing using manual categorization include an expenditure of user time and effort that may greatly exceed the time and effort it takes to simply pose a question. Some questions may be too complex to easily text match or categorize. Such complex questions may require multiple text matching tags or may be difficult for a user to manually categorize and may therefore require additional processing time on the part of the text matching mechanism or user. These burdens may discourage a significant fraction of potential users from using the manual categorization routing system. Furthermore, the additional input provided by unsophisticated users in relation to complex questions may not advance, and may even be detrimental to the advancement of the proper categorization of a complex question.
An alternate scheme employed by some Q&A communities is to auto-categorize a question textually and route it to an expert assigned to that category (where the experts are directly tagged with category titles, or where each expert's tags are also auto-categorized).
A drawback to this approach is that the categories available via auto-categorization tend to be limited in number and broad in scope. Exemplary auto-categorization categories include “law,” “sports,” and “history.” Such broad categories lack the specificity to be accurately matched with a given question and there is a limited likelihood that a given expert's expertise will closely match the question content.
Even if numerous finely-divided categories could be created, populated, and transparently and unambiguously named for ease of selection (editorially or by some automated method), there is no provision for matching a question that falls into two categories to an expert that happens to be proficient in both. For example, the question: “Can I serve Chianti with pasta carbonara?” would ideally be sent to an expert in both “wine” and “Italian food.” Under presently available auto-categorization systems, such a question would typically be matched to an expert in either “wine” or “Italian food.” Furthermore, under such an auto-categorization system, a weighting system to categorize ambiguous terms would be necessary (e.g., does “bass” in a query imply the “fishing,” “music,” or “beer and ale” category?).
The present invention discloses a system and method wherein a question posed by a user may be explicitly routed to one or more entities that are presumably “experts” on the topic or topics related to the posed question. One or more of these experts may then respond privately or publicly to the question.